Deformable Image Research Articles

Feminism Up on Stage A Discussion on the Deformation of Female Images in Chinese Film and TV Works

Taking Chinese film and TV works as the research subject, this paper attempts to analyze the development of feminism and the deformation of the female image shaped by film and TV works, summarize and reflect on the motives as well as the results of these works. We deploy a case-study methodology to scrutinize the evolution of feminism and the prevailing trend of distorted female portrayals in film and TV. It identifies three primary modes of distortion in the depiction of women in three mediums. This paper also aims to advocate that film and TV works should reasonably shape female image, veritably reflect the voice of females and convey feminism, and positively promote the development of feminism in China. At the same time, we advocate that filmmakers ought to fathom the true essence of feminism, genuinely reflecting the authentic voices and demands of women. We also hope that this paper will have a deeper significance for the subsequent research.

Quantification of perineural satellitosis in pretreatment glioblastoma with structural MRI and a diffusion tensor imaging template.

Survival outcomes for glioblastoma (GBM) patients remain unfavorable, and tumor recurrence is often observed. Understanding the radiological growth patterns of GBM could aid in improving outcomes. This study aimed to examine the relationship between contrast-enhancing tumor growth direction and white matter, using an image registration and deformation strategy. In GBM patients 2 pretreatment scans (diagnostic and neuronavigation) were gathered retrospectively, and coregistered to a template and diffusion tensor imaging (DTI) atlas. The GBM lesions were segmented and coregistered to the same space. Growth vectors were derived and divided into vector populations parallel (Φ = 0-20°) and perpendicular (Φ = 70-90°) to white matter. To test for statistical significance between parallel and perpendicular groups, a paired samples Student's t-test was performed. O6-methylguanine-DNA methyltransferase (MGMT) methylation status and its correlation to growth rate were also tested using a one-way ANOVA test. For 78 GBM patients (mean age 61 years ± 13 SD, 32 men), the included GBM lesions showed a predominant preference for perineural satellitosis (P < .001), with a mean percentile growth of 30.8% (95% CI: 29.6-32.0%) parallel (0° < |Φ| < 20°) to white matter. Perpendicular tumor growth with respect to white matter microstructure (70° < |Φ| < 90°) showed to be 22.7% (95% CI: 21.3-24.1%) of total tumor growth direction. The presented strategy showed that tumor growth direction in pretreatment GBM patients correlated with white matter architecture. Future studies with patient-specific DTI data are required to verify the accuracy of this method prospectively to identify its usefulness as a clinical metric in pre and posttreatment settings.

Dose-volume relationships of planned versus estimated delivered radiation doses to pelvic organs at risk and side effects in patients treated with salvage radiotherapy for recurrent prostate cancer

PurposeTo investigate estimated delivered dose distributions using weekly cone-beam computed tomography (CBCT) scans for pelvic organs at risk (OARs) in salvage radiotherapy (SRT) after radical prostatectomy. Furthermore, to compare them with the originally planned dose distributions and analyse associations with gastrointestinal (GI) and genitourinary (GU) side effects. MethodsThis study is part of a phase II trial involving SRT for recurrent prostate cancer. Treatment was personalised based on PSA response during SRT, classifying patients as PSA responders or non-responders. Estimated radiation dose distributions were obtained using deformable image registration from weekly CBCT scans. GI and GU toxicities were assessed using the RTOG toxicity scale, while patient-reported symptoms were monitored through self-assessment questionnaires. ResultsThe study included 100 patients, with similar treatment-related side effects observed in both responders and non-responders. Differences in dose-volume metrics between the planned and estimated delivered doses for the examined OARs were mostly modest, although generally statistically significant. We identified statistically significant associations between QUANTEC-recommended dose-volume constraints and acute bowel toxicity, as well as late urinary patient-reported symptoms, for both the estimated delivered and planned dose distributions. ConclusionWe found small but statistically significant differences between estimated delivered and planned doses to OARs. These differences showed trends toward improved associations for estimated delivered dose distributions with side effects. Enhanced registration methods and imaging techniques could potentially further enhance the assessment of truly delivered doses and yield more reliable dose-volume constraints for future therapies.

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy

Objective. Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the use of deformable image registration (DIR) for contour propagation and dose accumulation. However, inconsistencies in DIR solutions between different algorithms have caused distrust, hampering its direct clinical use. This work aims to enable the clinical use of DIR by developing deep learning methods to predict DIR uncertainty and propagating it into clinically usable metrics. Approach. Supervised and unsupervised neural networks were trained to predict the Gaussian uncertainty of a given deformable vector field (DVF). Since both methods rely on different assumptions, their predictions differ and were further merged into a combined model. The resulting normally distributed DVFs can be directly sampled to propagate the uncertainty into contour and accumulated dose uncertainty. Main results. The unsupervised and combined models can accurately predict the uncertainty in the manually annotated landmarks on the DIRLAB dataset. Furthermore, for 5 patients with lung cancer, the propagation of the predicted DVF uncertainty into contour uncertainty yielded for both methods an expected calibration error of less than 3%. Additionally, the probabilisticly accumulated dose volume histograms (DVH) encompass well the accumulated proton therapy doses using 5 different DIR algorithms. It was additionally shown that the unsupervised model can be used for different DIR algorithms without the need for retraining. Significance. Our work presents first-of-a-kind deep learning methods to predict the uncertainty of the DIR process. The methods are fast, yield high-quality uncertainty estimates and are useable for different algorithms and applications. This allows clinics to use DIR uncertainty in their workflows without the need to change their DIR implementation.

Volumetric feature points integration with bio-structure-informed guidance for deformable multi-modal CT image registration

Objective. Medical image registration represents a fundamental challenge in medical image processing. Specifically, CT-CBCT registration has significant implications in the context of image-guided radiation therapy (IGRT). However, traditional iterative methods often require considerable computational time. Deep learning based methods, especially when dealing with low contrast organs, are frequently entangled in local optimal solutions. Approach. To address these limitations, we introduce a registration method based on volumetric feature points integration with bio-structure-informed guidance. Surface point cloud is generated from segmentation labels during the training stage, with both the surface-registered point pairs and voxel feature point pairs co-guiding the training process, thereby achieving higher registration accuracy. Main results. Our findings have been validated on paired CT-CBCT datasets. In comparison with other deep learning registration methods, our approach has improved the precision by 6%, reaching a state-of-the-art status. Significance. The integration of voxel feature points and bio-structure feature points to guide the training of the medical image registration network has achieved promising results. This provides a meaningful direction for further research in medical image registration and IGRT.

Confinement-Modulated Clusterization-Triggered Time-Dependent Phosphorescence Color from Xylan-Carbonized Polymer Dots.

Achieving time-dependent phosphorescence color (TDPC) in organic materials is attractive but extremely challenging due to the nonradiative decay and modulation puzzle of triplet state. Herein, xylan, a hemicellulose waste from the paper mill, was used to construct carbonized polymer dots (CPDs) with clusterization-triggered room-temperature phosphorescence (RTP). CPDs were endowed with tuneable triplet energy levels by through-space conjugation of heteroatom groups, which could be confined in silica to simultaneously activate surface oxide-related low-energy and cross-linked core N-related high-energy emissive centers. Thus, the blue emissive center with a lifetime of 425.6 ms and green emissive center with a longer lifetime of 1506 ms coexisted in the confined CPDs; the former was the dominant contribution to RTP at first, and the latter became dominant over time, leading to a typical TDPC evolution with large color contrast from blue to blue-green and then to green. Meanwhile, the TDPC could remain unobstructed after the confined CPDs were soaked in water for more than a month. The CPDs were successfully applied in location and deformation imaging of hydrogel and advanced dynamic information encryption and anticounterfeiting. The work may shed new light on the design of TDPC materials and broaden the high-value use of paper-mill waste xylan.

Deep-learning-based deformable image registration of head CT and MRI scans

This work is motivated by the lack of publications on the direct application of multimodal image registration with deep-learning techniques for the enhancement of treatment planning in particle therapy. An unsupervised workflow, which seeks to improve image alignment, was developed and evaluated for computed tomography and magnetic resonance imaging scans of the head. The scans of 39 paediatric patients with brain tumours were available. The focus of the two-step workflow, including preprocessing of the scans for normalisation, is deformable image registration (DIR) with a deep neural network, which generates deformation vector fields (DVFs). To obtain a suitable configuration of the network, parameter tuning is performed by varying its parameters, e.g., layer size, regularisation (λ) of the DVF and learning rate (α). Image similarity was determined with the Dice similarity coefficient, mDSC, using segmented images and the mutual-information metric, mMI. The performance of the deep-learning models was assessed with the inverse consistency, mIC, and the Jacobian determinant, mJD. Inverse consistency is obtained for mIC = 0 mm, while the determinant of a deformed image is expected to be unity. The deep-learning models passed both performance checks, indicated by the mean values m̄IC=(0.57±1.00)mm and m̄JD=(1.00±0.07). Models with λ ≥ 1 yielded higher mDSC values than models with lower λ values. A small-architecture model with α = 10–4 was found to be most suitable for DIR, as improvement in image similarity of up to 12% was obtained in terms of mMI. The direct application of deep-learning models produced registered images improving image alignment between scans of different modalities.

ORRN: An ODE-Based Recursive Registration Network for Deformable Respiratory Motion Estimation With Lung 4DCT Images.

Deformable Image Registration (DIR) plays a significant role in quantifying deformation in medical data. Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images. However, in 4D (3D + time) medical data, organ motion, such as respiratory motion and heart beating, can not be effectively modeled by pair-wise methods as they were optimized for image pairs but did not consider the organ motion patterns necessary when considering 4D data. This article presents ORRN, an Ordinary Differential Equations (ODE)-based recursive image registration network. Our network learns to estimate time-varying voxel velocities for an ODE that models deformation in 4D image data. It adopts a recursive registration strategy to progressively estimate a deformation field through ODE integration of voxel velocities. We evaluate the proposed method on two publicly available lung 4DCT datasets, DIRLab and CREATIS, for two tasks: 1) registering all images to the extreme inhale image for 3D+t deformation tracking and 2) registering extreme exhale to inhale phase images. Our method outperforms other learning-based methods in both tasks, producing the smallest Target Registration Error of 1.24 mm and 1.26 mm, respectively. Additionally, it produces less than 0.001% unrealistic image folding, and the computation speed is less than 1 s for each CT volume. ORRN demonstrates promising registration accuracy, deformation plausibility, and computation efficiency on group-wise and pair-wise registration tasks. It has significant implications in enabling fast and accurate respiratory motion estimation for treatment planning in radiation therapy or robot motion planning in thoracic needle insertion.

Comparison of consecutive and restained sections for image registration in histopathology.

Although the registration of restained sections allows nucleus-level alignment that enables a direct analysis of interacting biomarkers, consecutive sections only allow the transfer of region-level annotations. The latter can be achieved at low computational cost using coarser image resolutions. In digital histopathology, virtual multistaining is important for diagnosis and biomarker research. Additionally, it provides accurate ground truth for various deep-learning tasks. Virtual multistaining can be obtained using different stains for consecutive sections or by restaining the same section. Both approaches require image registration to compensate for tissue deformations, but little attention has been devoted to comparing their accuracy. We compared affine and deformable variational image registration of consecutive and restained sections and analyzed the effect of the image resolution that influences accuracy and required computational resources. The registration was applied to the automatic nonrigid histological image registration (ANHIR) challenge data (230 consecutive slide pairs) and the hyperparameters were determined. Then without changing the parameters, the registration was applied to a newly published hybrid dataset of restained and consecutive sections (HyReCo, 86 slide pairs, 5404 landmarks). We obtain a median landmark error after registration of (HyReCo) and (ANHIR) between consecutive sections. Between restained sections, the median registration error is 2.2 and in the two subsets of the HyReCo dataset. We observe that deformable registration leads to lower landmark errors than affine registration in both cases (), though the effect is smaller in restained sections. Deformable registration of consecutive and restained sections is a valuable tool for the joint analysis of different stains.

Adaptation Time as a Determinant of the Dosimetric Effectiveness of Online Adaptive Radiotherapy for Bladder Cancer

Interfraction anatomic deformations decrease the precision of radiotherapy, which can be improved by online adaptive radiation therapy (oART). However, oART takes time, allowing intrafractional deformations. In this study on focal radiotherapy for bladder cancer, we analyzed the time effect of oART on the equivalent uniform dose in the CTV (EUDCTV) per fraction and for the accumulated dose distribution over a treatment series as measure of effectiveness. A time-dependent digital CTV model was built from deformable image registration (DIR) between pre- and post-adaptation imaging. The model was highly dose fraction-specific. Planning target volume (PTV) margins were varied by shrinking the clinical PTV to obtain the margin-specific CTV. The EUDCTV per fraction decreased by—4.4 ± 0.9% of prescribed dose per min in treatment series with a steeper than average time dependency of EUDCTV. The EUDCTV for DIR-based accumulated dose distributions over a treatment series was significantly dependent on adaptation time and PTV margin (p &lt; 0.0001, Chi2 test for each variable). Increasing adaptation times larger than 10 min by five minutes requires a 1.9 ± 0.24 mm additional margin to maintain EUDCTV for a treatment series. Adaptation time is an important determinant of the precision of oART for one half of the bladder cancer patients, and it should be aimed at to be minimized.

Residual Aligner-based Network (RAN): Motion-separable structure for coarse-to-fine discontinuous deformable registration

Deformable image registration, the estimation of the spatial transformation between different images, is an important task in medical imaging. Deep learning techniques have been shown to perform 3D image registration efficiently. However, current registration strategies often only focus on the deformation smoothness, which leads to the ignorance of complicated motion patterns (e.g., separate or sliding motions), especially for the intersection of organs. Thus, the performance when dealing with the discontinuous motions of multiple nearby objects is limited, causing undesired predictive outcomes in clinical usage, such as misidentification and mislocalization of lesions or other abnormalities. Consequently, we proposed a novel registration method to address this issue: a new Motion Separable backbone is exploited to capture the separate motion, with a theoretical analysis of the upper bound of the motions’ discontinuity provided. In addition, a novel Residual Aligner module was used to disentangle and refine the predicted motions across the multiple neighboring objects/organs. We evaluate our method, Residual Aligner-based Network (RAN), on abdominal Computed Tomography (CT) scans and it has shown to achieve one of the most accurate unsupervised inter-subject registration for the 9 organs, with the highest-ranked registration of the veins (Dice Similarity Coefficient (%)/Average surface distance (mm): 62%/4.9mm for the vena cava and 34%/7.9mm for the portal and splenic vein), with a smaller model structure and less computation compared to state-of-the-art methods. Furthermore, when applied to lung CT, the RAN achieves comparable results to the best-ranked networks (94%/3.0mm), also with fewer parameters and less computation.

Structural response of steel sandwich panels with PUR foam core subjected to edgewise compression: Experimental, numerical, and analytical considerations

With the objective of leading to innovative lightweight structural components in the construction sector using sandwich panels with polyurethane foam and steel face sheets, different studies were conducted on such structures, including experimental, numerical, and analytical campaigns. The experimental campaign includes the mechanical characterisation of the constituent materials and the edgewise compressive test of small-scale sandwich panels. The test protocols are detailed, and their influence on the results is assessed. Through the use of digital image correlation recordings, deformation patterns and failure mechanisms in the constituent materials and the composite structure are identified. Material constitutive models are calibrated based on the experimentally obtained results. The accuracy of such models is first evaluated against the stress–strain curves recorded during the experiments. In the second stage, the response of the sandwich panels subjected to edgewise compressive loading is simulated. A parametric analysis on the sensitivity of the numerical models to the initial geometrical imperfections is shown to be fundamental to capturing the behaviour of the sandwich panels. Furthermore, the experimental and numerical results are compared to analytical solutions of the problem of the edgewise compression of sandwich panels. Finally, a thorough discussion of the obtained experimental, numerical and analytical results is presented, along with comparisons to results available in the literature.

Evaluation of a novel CBCT conversion method implemented in a treatment planning system

BackgroundTo evaluate a novel CBCT conversion algorithm for dose calculation implemented in a research version of a treatment planning system (TPS).MethodsThe algorithm was implemented in a research version of RayStation (v. 11B-DTK, RaySearch, Stockholm, Sweden). CBCTs acquired for each ten head and neck (HN), gynecology (GYN) and lung cancer (LNG) patients were collected and converted using the new algorithm (CBCTc). A bulk density overriding technique implemented in the same version of the TPS was used for comparison (CBCTb). A deformed CT (dCT) was created by using deformable image registration of the planning CT (pCT) to the CBCT to reduce anatomical changes. All treatment plans were recalculated on the pCT, dCT, CBCTc and the CBCTb. The resulting dose distributions were analyzed using the MICE toolkit (NONPIMedical AB Sweden, Umeå) with local gamma analysis, with 1% dose difference and 1 mm distance to agreement criteria. A Wilcoxon paired rank sum test was applied to test the differences in gamma pass rates (GPRs). A p value smaller than 0.05 considered statistically significant.ResultsThe GPRs for the CBCTb method were systematically lower compared to the CBCTc method. Using the 10% dose threshold and the dCT as reference the median GPRs were for the CBCTc method were 100% and 99.8% for the HN and GYN cases, respectively. Compared to that the GPRs of the CBCTb method were lower with values of 99.8% and 98.0%, for the HN and GYN cases, respectively. The GPRs of the LNG cases were 99.9% and 97.5% for the CBCTc and CBCTb method, respectively. These differences were statistically significant. The main differences between the dose calculated on the CBCTs and the pCTs were found in regions near air/tissue interfaces, which are also subject to anatomical variations.ConclusionThe dose distribution calculated using the new CBCTc method showed excellent agreement with the dose calculated using dCT and pCT and was superior to the CBCTb method. The main reasons for deviations of the calculated dose distribution were caused by anatomical variations between the pCT and the corrected CBCT.

Improved imaging of ground deformation and brine seepage around abandoned flooded salt mines by joint inversion of multiphysics data

Centuries of bad mine-flooding practice have left a legacy of underground salt mine collapse and contaminated discharges, the scale of which is only slowly being revealed now that ground processes have caused instability over decades to >100 years. Geophysical methods are used for mapping ground subsidence and groundwater contamination at such sites but suffer individually from non-uniqueness and with the underlying mechanism and processes not being well understood. Here, to reduce uncertainty and maximize accuracy we recover subsurface models with structural similarity enforced via crossgradients joint inversion of seismic refraction and dc resistivity data for the top 40 m at former mining areas in Carrickfergus region of Northern Ireland. The models in combination with multispectral image fusion, enable us to identify hitherto unknown mechanism of deformation and compositional changes consistent with the Chebotarev geochemical evolution of groundwater. We found evidence of concealed gravitational slump structures interpreted as being caused by water ingress weakening the gypsum-bearing marl bedrock (via gypsum dissolution and increased fluid pressures within marl) and leading to gravitational gliding deformation, expressed as curved subsidence bands and stepped thrusts coincident with surface zones of brine-mud seepage and sinkhole collapse. Our results have wide-ranging implications for best-practice assessment and management of abandoned flooded salt mines and the hazards they pose to ground stability and water resources world-wide.

Left atrial dysfunction during stress in patients with atrial fibrillation

Abstract Background Left atrial (LA) myopathy with paroxysmal and permanent atrial fibrillation (AF) is frequent in chronic coronary syndromes (CCS). Objective This study sought to assess LA function at rest and during stress across the spectrum of AF. Methods In a prospective, multicentric, observational study design, we studied a cohort of 2391 patients (age=67±12 years; 1459, 61% male): 2142 (89.6%) were in sinus rhythm (SR, Group 1), 165 (6.9%) had a history of paroxysmal AF (Group 2) and 84 (3.5%) were in permanent AF (Group 3). All patients were referred for CCS and underwent stress echocardiography (SE) with exercise (n=1092, 45.3%), dobutamine (n=320, 13.4%) or vasodilators (n= 989, 40.3%: dipyridamole n=963 and adenosine n=26). Left atrial volume index (LAVI) and LA reservoir strain (LASr) were assessed at rest and peak stress. Analysis with different vendors was performed offline on anonymized cine-loops by observers unaware of the study hypothesis. Results The progressive increase in LA dysfunction from Group 1 to 3 was accompanied by greater signs of pulmonary congestion and LV dysfunction (see Table 1). In particular, LASr progressively decreased from Group 1 to 3, both at rest and during stress, regardless of the stress employed (see Figure 1). Conclusions LA reservoir function can be easily assessed with deformation imaging at rest and during exercise or pharmacological stress echocardiography. LA dysfunction progressively increases from SR to paroxysmal to permanent AF and is associated with more important LV systolic dysfunction, LV filling pressure, and pulmonary congestion in CCS.Table 1.Echocardiographic parametersFigure 1.LAVI and LASr at rest and peak

Echocardiographic deformation imaging as a substitute for cardiac magnetic resonance imaging in arrhythmogenic right ventricular cardiomyopathy risk prediction

Abstract Background &amp; aims Echocardiography has a central role in diagnosis and follow-up of patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). It is however not included in the risk calculator for ventricular arrhythmia (VA) which was recently developed and validate, since traditional echo parameters were outperformed by RV ejection fraction (RVEF) measured by CMR. CMR is however not as widely available as echocardiography. We aimed to investigate whether echocardiographic RV deformation imaging could be used as a substitute for RVEF by CMR in arrhythmic risk prediction in ARVC. Methods &amp; results From two referral centres, 150 consecutive patients with a definite ARVC diagnosis, no prior sustained VA and an echocardiogram suitable for deformation analysis were included (aged 41 ± 17 years, 50% female). During a median follow-up of 6.3 (IQR 3.1-9.8) years, 37 (25%) experienced a first-time sustained VA. The current ARVC risk calculator, including RVEF by CMR performed well in this cohort, with an optimism corrected C-statistic of 0.77 (95% CI 0.71 – 0.84). When RVEF was replaced by RV free wall longitudinal strain, the latter was an independent predictor (p = 0.005) and C-statistic changed to 0.80 (95% CI 0.74 – 0.86). The model including RV deformation imaging reduced the Akaike information criterion by &amp;gt;2. Conclusions This study showed that echocardiographic RV deformation imaging is able to replace RVEF by CMR in ARVC risk prediction without losing discriminative power. In clinical use, repeated yearly risk assessment using echocardiography could be alternated with a CMR every few years.

An individual's skin stiffness predicts their tactile discrimination of compliance.

Individual differences in tactile acuity have been correlated with age, gender and finger size, whereas the role of the skin's stiffness has been underexplored. Using an approach to image the 3-D deformation of the skin surface during contact with transparent elastic objects, we evaluate a cohort of 40 young participants, who present a diverse range of finger size, skin stiffness and fingerprint ridge breadth. The results indicate that skin stiffness generally correlates with finger size, although individuals with relatively softer skin can better discriminate compliant objects. Analysis of contact at the skin surface reveals that softer skin generates more prominent patterns of deformation, in particular greater rates of change in contact area, which correlate with higher rates of perceptual discrimination of compliance, regardless of finger size. Moreover, upon applying hyaluronic acid to soften individuals' skin, we observe immediate, marked and systematic changes in skin deformation and consequent improvements in perceptual acuity in differentiating compliance. Together, the combination of 3-D imaging of the skin surface, biomechanics measurements, multivariate regression and clustering, and psychophysical experiments show that subtle distinctions in skin stiffness modulate the mechanical signalling of touch and shape individual differences in perceptual acuity. KEY POINTS: Although declines in tactile acuity with ageing are a function of multiple factors, for younger people, the current working hypothesis has been that smaller fingers are better at informing perceptual discrimination because of a higher density of neural afferents. To decouple relative impacts on tactile acuity of skin properties of finger size, skin stiffness, and fingerprint ridge breadth, we combined 3-D imaging of skin surface deformation, biomechanical measurements, multivariate regression and clustering, and psychophysics. The results indicate that skin stiffness generally correlates with finger size, although it more robustly correlates with and predicts an individual's perceptual acuity. In particular, more elastic skin generates higher rates of deformation, which correlate with perceptual discrimination, shown most dramatically by softening each participant's skin with hyaluronic acid. In refining the current working hypothesis, we show the skin's stiffness strongly shapes the signalling of touch and modulates individual differences in perceptual acuity.

Speckle tracking echocardiography detects subtle right ventricular systolic dysfunction in post Covid syndrome after mild severity Covid-19

Abstract Introduction Now that the global COVID-19 pandemic has come to an end, many patients seek medical help for its aftermath giving rise to post COVID syndrome. Post COVID syndrome describes individuals with a history of probable or confirmed COVID-19 infection, usually 3 months from the onset of infection with symptoms that last for at least 2 months and no alternative diagnosis can be explained. The affection of the right ventricular (RV) systolic function is a cornerstone in the pathogenesis underlying Post COVID syndrome. Many modalities were used to assess the RV with a widespread debate as to the most accurate one. However, echocardiography remains the modality of choice for the evaluation of RV systolic function, especially when using its novel features as myocardial deformation imaging by speckle tracking echocardiography (STE) to surpass the RV’s peculiar anatomy, retrosternal position and complex architecture. Purpose The aim was to assess RV systolic function by STE 3-6 months after recovery from mild severity COVID-19 disease and correlate these findings with Post COVID syndrome. Methods This study included 96 adults (above 18 years of age) who were classified into three equal groups; the first group recruited 32 subjects 3-6 months after recovery from mild severity COVID-19 disease without post COVID syndrome, the second group included 32 subjects 3-6 months after recovery from mild severity COVID-19 disease with post COVID syndrome and the third group recruited 32 gender-matched healthy subjects of similar ages without previous COVID-19 disease (control group). Each subject underwent a detailed transthoracic echocardiography including RV function assessment using 2D RV STE imaging. Results RV global longitudinal strain (GLS) and RV free wall longitudinal strain (FWLS) were significantly lower in patients after recovery from mild severity COVID-19 disease with post COVID syndrome (P-value= 0.010) in comparison to the other groups. There was a statistically significant negative correlation found between RV 2D STE parameters and RV end-diastolic dimension, systolic pulmonary artery pressure and RV myocardial performance index. Also, there was a statistically significant positive correlation found between RV 2D STE parameters and RV fractional area change, tricuspid annular plane systolic excursion and RV S'. The cardiac symptomatology in patients with post COVID syndrome was analyzed showing that the most common three symptoms were fatigue, palpitations and chest pain with a prevalence of 65.6%, 40.6% and 31% respectively. These three symptoms showed a significant negative relation with RV-GLS and RV-FWLS. Conclusion RV function assessed using STE occupied the lower normal ranges in patients recovering from mild severity COVID-19 disease, particularly those with post COVID syndrome. 2D STE may be used for the detection and follow-up of possible subtle long term RV dysfunction after mild severity COVID-19 disease.RV-GLS and RV-FWLS in the studied groupsRV-GLS and RV-FWLS using 2D STE

The added value of abnormal regional myocardial function for risk prediction in arrhythmogenic right ventricular cardiomyopathy

Abstract Background &amp; aims A risk calculator for individualized prediction of first-time sustained ventricular arrhythmia (VA) in arrhythmogenic right ventricular cardiomyopathy (ARVC) patients has recently been developed and validated. This study aimed to investigate whether regional functional abnormalities, measured by echocardiographic deformation imaging, can provide additional prognostic value. Methods &amp; results From two referral centres, 150 consecutive patients with a definite ARVC diagnosis, no prior sustained VA and an echocardiogram suitable for deformation analysis were included (aged 41 ± 17 years, 50% female). During a median follow-up of 6.3 (IQR 3.1-9.8) years, 37 (25%) experienced a first-time sustained VA. All tested left and right ventricular (LV, RV) deformation parameters were univariate predictors for first-time VA. While LV function did not add predictive value in multivariate analysis, two RV deformation parameters did; RV free wall longitudinal strain and regional RV deformation patterns remained independent predictors after adjusting for the calculator-predicted risk (HR 1.07 [1.02-1.11]; p = 0.004 and 4.45 [1.07-18.57]; p = 0.040, respectively) and improved its discriminative value (from C-statistic 0.78 to 0.82 in both. Akaike information criterion change &amp;gt;2). Importantly, all patients who experienced VA within 5 years from the echocardiographic assessment had abnormal regional RV deformation patterns at baseline. Conclusions This study showed that regional functional abnormalities measured by echocardiographic deformation imaging can further refine personalized arrhythmic risk prediction when added to the ARVC risk calculator. The excellent negative predictive value of normal RV deformation could support a watchful waiting strategy regarding ICD implantation in intermediate risk patients.Added value of functional abnormalities

Patterns of failure for recurrent head and neck squamous cell carcinoma treated with salvage surgery and postoperative IMRT reirradiation

Purpose/ObjectivesThe purpose of this study was to evaluate patterns of locoregional recurrence (LRR) after surgical salvage and adjuvant reirradiation with IMRT for recurrent head and neck squamous cell cancer (HNSCC). Materials/MethodsPatterns of LRR for 61 patients treated consecutively between 2003 and 2014 who received post-operative IMRT reirradiation to ≥ 60 Gy for recurrent HNSCC were determined by 2 methods: 1) physician classification via visual comparison of post-radiotherapy imaging to reirradiation plans; and 2) using deformable image registration (DIR). Those without evaluable CT planning image data were excluded. All recurrences were verified by biopsy or radiological progression. Failures were defined as in-field, marginal, or out-of-field. Logistic regression analyses were performed to identify predictors for LRR. ResultsA total of 55 patients were eligible for analysis and 23 (42 %) had documented LRR after reirradiation. Location of recurrent disease prior to salvage surgery (lymphatic vs. mucosal) was the most significant predictor of LRR after post-operative reirradiation with salvage rate of 67 % for lymphatic vs. 33 % for mucosal sites (p = 0.037). Physician classification of LRR yielded 14 (61 %) in-field failures, 3 (13 %) marginal failures, and 6 (26 %) out-of-field failures, while DIR yielded 10 (44 %) in-field failures, 4 (17 %) marginal failures, and 9 (39 %) out-of-field failures. Most failures (57 %) occurred within the original site of recurrence or first echelon lymphatic drainage. Of patients who had a free flap placed during salvage surgery, 56 % of failures occurred within 1 cm of the surgical flap. ConclusionOur study highlights the role of DIR in enhancing the accuracy and consistency of POF analysis. Compared to traditional visual inspection, DIR reduces interobserver variability and provides more nuanced insights into dose-specific and spatial parameters of locoregional recurrences. Additionally, the study identifies the location of the initial recurrence as a key predictor of subsequent locoregional recurrence after salvage surgery and re-IMRT.